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Summertime Air-Sea Interactions In The Tropical Indian Ocean And Their Associations With The General Circulation Variations In The Northern Hemisphere As Revealed By SVD Analysis

Posted on:2007-03-24Degree:MasterType:Thesis
Country:ChinaCandidate:G C WangFull Text:PDF
GTID:2120360182483290Subject:Science of meteorology
Abstract/Summary:PDF Full Text Request
Based on the monthly mean data from NCEP/NCAR reanalysis, sea surface temperature from GISST data set as well as 160 station precipitation data in China , we have investigated the air-sea interactions in the tropical Indian Ocean and their relations with the climate variations in both the tropical Pacific and the northern hemisphere. The results are summarized as follows:(1) The Indian Ocean is well coupled with the atmosphere as indicated by SVD1 and SVD2 modes. The SVD analysis has been performed for SST and wind at 850hPa in the Indian Ocean. SVD1 and SVD2 explain large parts of the Indian Ocean variabilities. The first mode (SVD1) displays the lag response of the Indian Ocean to El Nino in Pacific, characterized with the same singed SSTA in the basin-wide Indian Ocean. The second mode (SVD2) related to IOD displays an air-sea coupled mode in Indian Ocean, characterized with the dipole-like pattern in SSTA. The SVD2 is well correlated with both the IODMI and Nino3 index, showing some statistical relations between IOD and ENSO as reported in some recent references.(2) The strong correlations have been observed between the Indian Ocean SSTA and the geopotential height field at 500hPa in the northern hemisphere. The response of the Indian Ocean SSTA with the time lag by 4-6 months to the variations of global geopotential height at 500hPa is revealled. The left component of SVD1 shows a mono-polar mode in the tropical Indian Ocean, whereas the right component (for geopotential height) of SVD1 displays a PNA-like pattern over Pacific-North America regions, indicating the close relations of Indian Ocean SSTA with the ENSO. SVD2 shows its left component a mode as a mixture of the IOD and ENSO, indicating that the IOD is probably related to the anomalous changes of the global circulations.(3) The SVD analysis has been performed for the time-series of SSTA averaged over the tropical Indian Ocean and that averaged over the tropical Pacific. The results show a 4 months lag response of the Indian Ocean SSTA to the Pacific SSTA forcing.The SVD analysis for IOD and Nino3 indices shows-a high I0D-NIN03 correlation by the lag of 7 months. The SVD analysis for October-November-December-January mean Pacific SSTA and April-May-June mean Indian Ocean SSTA of the next year yields the SVD 1 (left component), a momo-polar mode of Indian Ocean SSTA, indepent of ENSO, and the SVD2(left component) the dipole pattern of Indian Ocean SSTA, a mixed mode of IOD and ENSO.(4) The relationships between Indian Ocean SST anomalies and the Summer rainfall in China have been examined. The time-lag correlations of IOD index with areal averaged precipitation in China show that the Indian Ocean SSTA has strong influences on the summertime precipitation in China. When there is a positive (negative) IOD event, the rainfall in June tends to increase (decrease) in Fujian and Sichuan Province. In other places, rainfall tends to decrease (increase). The patterns of the rainfall anomalies in June and July look almost the same. The precipitation in the Changjiang-Huaihe River region tends to be more (less) than normal and that in the middle and western part of China tends to be less (more) than normal. When the basin-wide positive (negative) anomalies of uniform polarity of SSTA dominate the Indian Ocean SSTA variability, the rainfall in Jiangnan, the Southern part of China and the southwestern part of China tends to be more (less) than normal and that in Huanghe-Huaihe River Regions and west of Sichuan province tends to be less (more) in June. The rainfall anomalies display a "positive-negative-positive" pattern from low-latitudes to high-latitudes in August.
Keywords/Search Tags:Indian Ocean Dipole IOD, ENSO, Singular Value Decomposition
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